10 Allegro.eu SQL Interview Questions (Updated 2024)
At Allegro.eu, one of the largest e-com sites in Europe, SQL does the heavy lifting for extracting customer data for targeted marketing campaigns and analyzing sales trends for inventory management. That's why Allegro.eu asks SQL questions in interviews for Data Analytics, Data Science, and Data Engineering jobs.
As such, to help you prepare for the Allegro.eu SQL interview, here’s 10 Allegro.eu SQL interview questions – able to answer them all?
10 Allegro.eu SQL Interview Questions
SQL Question 1: Customer Product Review Analysis
Suppose you are a data analyst at Allegro.eu and your task is to analyze the product reviews data. The data includes user_id, product_id, review submission date, and the star rating given by the user. Your task:
- Extract the month from the submission date.
- For each month, for each product, calculate the average star rating.
You should output a table with the month, product id, and the corresponding average star rating.
Example Input:
| review_id | user_id | submit_date | product_id | stars |
|---|---|---|---|---|
| 6171 | 123 | 2022-06-08 00:00:00 | 50001 | 4 |
| 7802 | 265 | 2022-06-10 00:00:00 | 69852 | 4 |
| 5293 | 362 | 2022-06-18 00:00:00 | 50001 | 3 |
| 6352 | 192 | 2022-07-26 00:00:00 | 69852 | 3 |
| 4517 | 981 | 2022-07-05 00:00:00 | 69852 | 2 |
Example Output:
| mth | product_id | avg_stars |
|---|---|---|
| 6 | 50001 | 3.50 |
| 6 | 69852 | 4.00 |
| 7 | 69852 | 2.50 |
Answer:
In this query, we first extract the month from the using the function. Then, we group the data by and . For each group, we calculate the average of using the function. Finally, we sort the output by and .
To solve a similar window function question on DataLemur's free online SQL code editor, solve this Google SQL Interview Question:
SQL Question 2: Calculate Total Sale Amount Per Category
Allegro.eu operates an online e-commerce platform. Suppose the company has a table that contains data about each product, including its ID, name, and category, and a table that captures every sale, including the product_id, transaction date, item quantity, and total amount (item quantity * transaction price).
Assume the goal is to provide a monthly report that calculates the total sale amount for each product category.
Sample Input:
| product_id | product_name | category |
|---|---|---|
| 1 | Widget A | Electronics |
| 2 | Widget B | Electronics |
| 3 | Gizmo C | Garden |
| 4 | Gizmo D | Garden |
| 5 | Gadget E | Kitchen |
Sample Input:
| transaction_id | transaction_date | product_id | quantity | total_amount |
|---|---|---|---|---|
| 1001 | 2021-12-01 | 1 | 2 | 200 |
| 1002 | 2021-12-02 | 2 | 3 | 300 |
| 1003 | 2022-01-15 | 3 | 1 | 100 |
| 1004 | 2022-01-18 | 4 | 5 | 500 |
| 1005 | 2022-01-20 | 5 | 3 | 300 |
Answer:
This SQL query first joins the and table on the column. Then, it groups by both (truncated to the month) and the category. After grouping, it calculates the sum of all total transaction amounts per category. Lastly, results are ordered by the month and total sales in descending order. The final output will provide the total sale amount for each product category per month.
SQL Question 3: Can you explain what a cross-join is and the purpose of using them?
A cross-join, also known as a cartesian join, is a JOIN that produces the cross-product of two tables. In a cross-join, each row from the first table is matched with every row from the second table, resulting in a new table with a row for each possible combination of rows from the two input tables.
Let's say you were building a Machine Learning model that attempts to score the probability of a customer purchasing a Allegro.eu product. Before working in Pandas and Tensorflow, you might want to do some Exploratory Data Analysis (EDA) in SQL, and generate all pairs of customers and Allegro.eu products.
Here's a cross-join query you could run:
Cross-joins are useful for generating all possible combinations, but they can also create huge tables if you're not careful. For instance, if you had 10,000 potential customers and Allegro.eu had 500 different product SKUs, the resulting cross-join would have 5 million rows!
SQL Question 4: Allegro Customer Order Filtering
For Allegro.eu, an e-commerce company, let's imagine you are a data analyst team member. You have been asked to generate a list of customers who have made a purchase within the last 30 days and those who made a purchase more than 60 days ago but not within the last 30 days. The company wants to use this data to send out marketing emails to these two distinct groups of customers.
Below is an example of a table, , containing historical customer order data:
Example Input:
| order_id | customer_id | product_id | order_date |
|---|---|---|---|
| 1 | 15 | 201 | 2022-09-06 |
| 2 | 23 | 354 | 2022-10-01 |
| 3 | 45 | 129 | 2022-09-15 |
| 4 | 23 | 201 | 2022-09-05 |
| 5 | 87 | 354 | 2022-08-15 |
You need to form two separate lists as and from this data.
Answer:
To get the 'recent customers' who made a purchase within the last 30 days:
To get the 'inactive customers' who made a purchase more than 60 days ago but not within the last 30 days:
In these queries, and are used to subtract 30 and 60 days respectively from the current date. keyword is used to ensure we don't record the same customer more than once in our lists. The first query generates a list of recent customers, and the second uses a subquery to exclude these recent customers from the inactive customers list.
SQL Question 5: Can you explain the concept of a constraint in SQL?
Constraints are just rules for your DBMS to follow when updating/inserting/deleting data.
Say you had a table of Allegro.eu employees, and their salaries, job titles, and performance review data. Here's some examples of SQL constraints you could implement:
NOT NULL: This constraint could be used to ensure that certain columns in the employee table, such as the employee's first and last name, cannot contain NULL values.
UNIQUE: This constraint could be used to ensure that the employee ID is unique. This would prevent duplicate entries in the employee table.
PRIMARY KEY: This constraint could be used to combine the NOT NULL and UNIQUE constraints to create a primary key for the employee table. The employee ID could serve as the primary key.
FOREIGN KEY: This constraint could be used to establish relationships between the employee table and other tables in the database. For example, you could use a foreign key to link the employee ID to the department ID in a department table to track which department each employee belongs to.
CHECK: This constraint could be used to ensure that certain data meets specific conditions. For example, you could use a CHECK constraint to ensure that salary values are always positive numbers.
DEFAULT: This constraint could be used to specify default values for certain columns. For example, you could use a DEFAULT constraint to set the employee hire date to the current date if no value is provided when a new employee is added to the database.
SQL Question 6: Average Sales Price Per Category
As an Allegro.eu employee, you are asked to provide valuable insights regarding the overall sales. Your specific task is to calculate the average sales price per category for all the listed items sold in the past month, sorted by the category.
Sample tables:
Example Input:
| sale_id | item_id | sale_date | sale_price |
|---|---|---|---|
| 1001 | 201 | 2022-09-01 | 50.00 |
| 1002 | 202 | 2022-09-03 | 75.00 |
| 1003 | 301 | 2022-09-04 | 120.00 |
| 1004 | 302 | 2022-09-06 | 100.00 |
| 1005 | 201 | 2022-09-10 | 45.00 |
Example Input:
| item_id | category |
|---|---|
| 201 | Electronics |
| 202 | Electronics |
| 301 | Books |
| 302 | Books |
Answer:
This query joins the sales and items tables on the item_id, and then filters for sales that happened in the last month. It then groups by the category and calculates the average sale price for each category. Finally, it sorts the result by category.
SQL Question 7: Can you describe the difference between a unique and a non-unique index?
Unique indexes help ensure that there are no duplicate key values in a table, maintaining data integrity. They enforce uniqueness whenever keys are added or changed within the index.
To define a unique index in PostgreSQL, you can use the following syntax:
To define a non-unique index in PostgreSQL, you can use the following syntax:
Non-unique indexes on the other hand, are used to improve query performance by maintaining a sorted order of frequently used data values, but they do not enforce constraints on the associated table.
SQL Question 8: Analyzing Click-Through-Rates for Allegro.eu
You are a Data Analyst at Allegro.eu, one of the largest online marketplace in Eastern Europe. The marketing department wants to understand how successful their product adverts are in the last 3 months in driving potential customers from viewing a product's advert to adding the product to the cart.
Specifically, they are interested in the click-through conversion rates, defined as the number of customers who viewed a product advertisement and subsequently added the product to the cart divided by the total number of customers who viewed the product advertisement.
Use the 'product_advert_views' and 'cart_adds' tables to answer the question.
Example Input:
| view_id | user_id | view_date | product_id |
|---|---|---|---|
| 2132 | 342 | 06/10/2022 00:00:00 | 11001 |
| 2133 | 784 | 06/11/2022 00:00:00 | 12002 |
| 2134 | 537 | 06/12/2022 00:00:00 | 11001 |
| 2135 | 183 | 06/13/2022 00:00:00 | 13003 |
| 2136 | 657 | 06/14/2022 00:00:00 | 12002 |
Example Input:
| add_id | user_id | add_date | product_id |
|---|---|---|---|
| 9821 | 537 | 06/12/2022 00:10:00 | 11001 |
| 9822 | 657 | 06/14/2022 00:11:00 | 12002 |
| 9823 | 237 | 06/15/2022 00:12:00 | 13003 |
| 9824 | 547 | 06/16/2022 00:13:00 | 11001 |
| 9825 | 169 | 06/17/2022 00:14:00 | 12002 |
Answer:
This SQL query first joins and on matching and . This will create a combined table with details of both views and adds for the same user and product.
We then group this combined table by and compute the conversion rate as the number of adds () divided by the number of views (). The multiplier of "1.0" is used to ensure the division result is a float (i.e., decimal) instead of an integer.
To solve another question about calculating rates, solve this TikTok SQL question on DataLemur's interactive SQL code editor:
SQL Question 9: Calculate the Total Sales Per Category
As a Data Analyst at Allegro.eu, your task is to determine the total sales for each product category for the last year. Each item belongs to a specific category and every time an item is sold, a transaction is recorded in the sales database. The sales database has the following schema:
Example Input
| product_id | product_name | category_id |
|---|---|---|
| 50001 | Apple iPhone 12 | 1 |
| 69852 | Samsung Galaxy S21 | 1 |
| 37890 | Dell XPS 15 | 2 |
| 98963 | HP Spectre x360 | 2 |
| 24567 | Canon EOS 5D Mark IV | 3 |
Example Input
| sale_id | product_id | sale_date | quantity | price |
|---|---|---|---|---|
| 9132 | 50001 | 06/08/2021 00:00:00 | 2 | 999 |
| 9845 | 69852 | 06/10/2021 00:00:00 | 1 | 800 |
| 7524 | 50001 | 06/18/2021 00:00:00 | 1 | 999 |
| 8213 | 37890 | 07/26/2021 00:00:00 | 3 | 1500 |
| 6541 | 98963 | 07/05/2021 00:00:00 | 1 | 1100 |
The objective is to write a query which groups the sales data by product category and then calculate the total sales (quantity * price) for each category.
Answer:
Example Output:
| category_id | total_sales |
|---|---|
| 1 | 5796 |
| 2 | 5600 |
| 3 | 0 |
This query joins the products and sales tables, before grouping the results by category. It then calculates the total sales per category and orders the final result by total sales in descending order. Notably, it only considers sales made in the year 2021.
SQL Question 10: Can you define what a database index is, and give some examples of different types of indexes?
A database index is a data structure that provides a quick lookup of data in a column or columns of a table.
There are several types of indexes that can be used in a database:
- Primary index: a unique identifier is used to access the row directly.
- Unique index: used to enforce the uniqueness of the indexed columns in a table.
- Composite index: created on multiple columns of a table, is used to speed up the search process for multiple columns
- Clustered index: determines the physical order of the data in a table
Preparing For The Allegro.eu SQL Interview
The key to acing a Allegro.eu SQL interview is to practice, practice, and then practice some more!
In addition to solving the above Allegro.eu SQL interview questions, you should also solve the 200+ tricky sql questions on DataLemur which come from companies like Amazon, Microsoft, Meta, and smaller tech companies.
Each problem on DataLemur has hints to guide you, step-by-step solutions and crucially, there is an online SQL code editor so you can instantly run your SQL query and have it graded.
To prep for the Allegro.eu SQL interview it is also wise to practice interview questions from other tech companies like:
However, if your SQL coding skills are weak, forget about going right into solving questions – go learn SQL with this SQL tutorial for Data Analytics.
This tutorial covers topics including filtering data with boolean operators and using wildcards with LIKE – both of these pop up often during Allegro.eu interviews.
Allegro.eu Data Science Interview Tips
What Do Allegro.eu Data Science Interviews Cover?
Beyond writing SQL queries, the other topics tested in the Allegro.eu Data Science Interview are:
- Probability & Stats Questions
- Python Pandas or R Coding Questions
- Open-Ended Data Case Studies
- Machine Learning Questions
- Behavioral Based Interview Questions
How To Prepare for Allegro.eu Data Science Interviews?
To prepare for Allegro.eu Data Science interviews read the book Ace the Data Science Interview because it's got:
- 201 interview questions sourced from tech companies like Google & Microsoft
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